class_docstr: Provides capabilities to run training using the DeepSpeed library, with training optimizations for large
      billion parameter models. `For more information: https://pytorch-
      lightning.readthedocs.io/en/stable/advanced/model_parallel.html#deepspeed`.
  
      .. warning:: ``DeepSpeedStrategy`` is in beta and subject to change.
  
      Defaults have been set to enable ZeRO-Offload and some have been taken from the link below.
      These defaults have been set generally, but may require tuning for optimum performance based on your model size.
      `For more information: https://www.deepspeed.ai/docs/config-json/#zero-optimizations-for-fp16-training`.
class_docstr_tok: ["Provides","capabilities","to","run","training","using","the","DeepSpeed","library",",","with","training","optimizations","for","large","billion","parameter","models",".","`","For","more","information",":","https",":","\/\/pytorch-","lightning.readthedocs.io\/en\/stable\/advanced\/model_parallel.html","#","deepspeed","`",".","..","warning",":",":","``","DeepSpeedStrategy","``","is","in","beta","and","subject","to","change",".","Defaults","have","been","set","to","enable","ZeRO-Offload","and","some","have","been","taken","from","the","link","below",".","These","defaults","have","been","set","generally",",","but","may","require","tuning","for","optimum","performance","based","on","your","model","size",".","`","For","more","information",":","https",":","\/\/www.deepspeed.ai\/docs\/config-json\/","#","zero-optimizations-for-fp16-training","`","."]
docstr: Parameters
  ----------
  zero_optimization
      Enable ZeRO optimization. This is only compatible with precision="16-mixed".
  
  stage
      Different stages of the ZeRO Optimizer. 0 is disabled,
      1 is optimizer state partitioning, 2 is optimizer+gradient state partitioning,
      3 is optimizer+gradient_parameter partitioning using the infinity engine.
  
  remote_device
      Device to instantiate the model on initially (``cpu`` or ``nvme``).
  
  offload_optimizer
      Enable offloading optimizer memory and computation to CPU or NVMe
      based on ``offload_optimizer_device``.
  
  offload_parameters
      When using ZeRO Stage 3, Enable offloading parameter memory and computation
      to CPU or NVMe based on ``offload_params_device``.
  
  offload_params_device
      When offloading parameters choose the device to offload to, ``cpu`` or ``nvme``.
  
  offload_optimizer_device
      When offloading optimizer state choose the device to offload to,
      ``cpu`` or ``nvme``.
  
  params_buffer_count
      Number of buffers in buffer pool for
      parameter offloading when ``offload_params_device`` is ``nvme``.
  
  params_buffer_size
      Size of buffers in buffer pool for parameter offloading
      when ``offload_params_device`` is ``nvme``.
  
  max_in_cpu
      Number of parameter elements to maintain in CPU memory when offloading to NVMe is enabled.
  
  nvme_path
      Filesystem path for NVMe device for optimizer/parameter state offloading.
  
  optimizer_buffer_count
      Number of buffers in buffer pool for optimizer state offloading
      when ``offload_optimizer_device`` is set to to ``nvme``.
      This should be at least the number of states maintained per parameter by the optimizer.
      For example, Adam optimizer has 4 states (parameter, gradient, momentum, and variance).
  
  block_size
      When using NVMe Offloading, the I/O block size in bytes.
  
  queue_depth
      When using NVMe Offloading, the I/O queue depth.
  
  single_submit
      When using NVMe Offloading,
      submit requests to storage device as multiple individual requests,
      as opposed to one block of requests.
  
  overlap_events
      When using NVMe Offloading,
      submit requests to storage device in an overlapped fashion
      without waiting for completion of earlier requests.
  
  thread_count
      When using NVMe Offloading,
      Intra-request parallelism for each read/write submitted by a user thread.
  
  pin_memory
      When using ZeRO stage 3, pin optimizer state memory on CPU.
      This could boost throughput at the cost of extra memory overhead.
  
  sub_group_size
      When using ZeRO stage 3, defines the number of parameters
      within a sub group to offload at a time.
      Smaller numbers require more communication, but improve memory efficiency.
  
  contiguous_gradients
      Copies gradients to a continuous buffer as they are produced.
      Avoids memory fragmentation during backwards. Useful when training large models.
  
  overlap_comm
      Overlap the reduction (synchronization) of gradients with the backwards computation.
      This is a speed optimization when training across multiple GPUs/machines.
  
  allgather_partitions: All gather updated parameters at the end of training step,
      instead of using a series of broadcast collectives.
  
  reduce_scatter: Use reduce/scatter instead of allreduce to average gradients.
  
  allgather_bucket_size
      Number of elements to allgather at once.
      Used to limit the memory required for larger model sizes, with a tradeoff with speed.
  
  reduce_bucket_size
      Number of elements to reduce at once.
      Used to limit the memory required for larger model sizes, with a tradeoff with speed.
  
  zero_allow_untested_optimizer
      Allow untested optimizers to be used with ZeRO. Currently only Adam is a
      DeepSpeed supported optimizer when using ZeRO.
  
  logging_batch_size_per_gpu
      Config used in DeepSpeed to calculate verbose timing for logging
      on a per sample per second basis (only displayed if logging=logging.INFO).
      If set to "auto", the plugin tries to infer this from
      the train DataLoader's BatchSampler, else defaults to 1.
      To obtain accurate logs when using datasets that do not support batch samplers,
      set this to the actual per gpu batch size (trainer.batch_size).
  
  config
      Pass in a deepspeed formatted config dict,
      or path to a deepspeed config: https://www.deepspeed.ai/docs/config-json.
      All defaults will be ignored if a config is passed in.
  
  logging_level
      Set logging level for deepspeed.
  
  loss_scale
      Loss scaling value for FP16 training.
      0.0 results in dynamic loss scaling, otherwise static.
  
  initial_scale_power
      Power of the initial dynamic loss scale value. Loss scale is computed
      by ``2^initial_scale_power``.
  
  loss_scale_window
      Window in which to raise/lower the dynamic FP16 loss scaling value.
  
  hysteresis
      FP16 Delay shift in Dynamic Loss scaling.
  
  min_loss_scale
      The minimum FP16 dynamic loss scaling value.
  
  partition_activations
      Enables partition activation when used with ZeRO stage 3 and model parallelism.
      Still requires you to wrap your forward functions in deepspeed.checkpointing.checkpoint.
      See `deepspeed tutorial
      <https://www.deepspeed.ai/tutorials/megatron/#deepspeed-activation-checkpoints-optional>`_.
  
  cpu_checkpointing
      Offloads partitioned activations to CPU if ``partition_activations`` is enabled.
  
  contiguous_memory_optimization
      Copies partitioned activations so that they are contiguous in memory.
      Not supported by all models.
  
  synchronize_checkpoint_boundary
      Insert :func:`torch.cuda.synchronize` at each checkpoint boundary.
  
  load_full_weights
      True when loading a single checkpoint file containing the model state dict
      when using ZeRO Stage 3. This differs from the DeepSpeed checkpoint which contains shards
      per worker.
docstr_tok: 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code: def __init__(
      self,
      accelerator: Optional[
          "pl.accelerators.accelerator.Accelerator"
      ] = None,
      zero_optimization: bool = True,
      stage: int = 2,
      remote_device: str = "cpu",
      offload_optimizer: bool = False,
      offload_parameters: bool = False,
      offload_params_device: str = "cpu",
      nvme_path: str = "/local_nvme",
      params_buffer_count: int = 5,
      params_buffer_size: int = 100_000_000,
      max_in_cpu: int = 1_000_000_000,
      offload_optimizer_device: str = "cpu",
      optimizer_buffer_count: int = 4,
      block_size: int = 1048576,
      queue_depth: int = 8,
      single_submit: bool = False,
      overlap_events: bool = True,
      thread_count: int = 1,
      pin_memory: bool = False,
      sub_group_size: int = 1_000_000_000_000,
      contiguous_gradients: bool = True,
      overlap_comm: bool = True,
      allgather_partitions: bool = True,
      reduce_scatter: bool = True,
      allgather_bucket_size: int = 200_000_000,
      reduce_bucket_size: int = 200_000_000,
      zero_allow_untested_optimizer: bool = True,
      logging_batch_size_per_gpu: Union[str, int] = "auto",
      config: Optional[Union[_PATH, Dict[str, Any]]] = None,
      logging_level: int = logging.WARN,
      parallel_devices: Optional[List[torch.device]] = None,
      cluster_environment: Optional[ClusterEnvironment] = None,
      loss_scale: float = 0,
      initial_scale_power: int = 16,
      loss_scale_window: int = 1000,
      hysteresis: int = 2,
      min_loss_scale: int = 1,
      partition_activations: bool = False,
      cpu_checkpointing: bool = False,
      contiguous_memory_optimization: bool = False,
      synchronize_checkpoint_boundary: bool = False,
      load_full_weights: bool = False,
      precision_plugin: Optional[PrecisionPlugin] = None,
      process_group_backend: Optional[str] = None,
  ) -> None:
      """
      Parameters
      ----------
      zero_optimization
          Enable ZeRO optimization. This is only compatible with precision="16-mixed".
  
      stage
          Different stages of the ZeRO Optimizer. 0 is disabled,
          1 is optimizer state partitioning, 2 is optimizer+gradient state partitioning,
          3 is optimizer+gradient_parameter partitioning using the infinity engine.
  
      remote_device
          Device to instantiate the model on initially (``cpu`` or ``nvme``).
  
      offload_optimizer
          Enable offloading optimizer memory and computation to CPU or NVMe
          based on ``offload_optimizer_device``.
  
      offload_parameters
          When using ZeRO Stage 3, Enable offloading parameter memory and computation
          to CPU or NVMe based on ``offload_params_device``.
  
      offload_params_device
          When offloading parameters choose the device to offload to, ``cpu`` or ``nvme``.
  
      offload_optimizer_device
          When offloading optimizer state choose the device to offload to,
          ``cpu`` or ``nvme``.
  
      params_buffer_count
          Number of buffers in buffer pool for
          parameter offloading when ``offload_params_device`` is ``nvme``.
  
      params_buffer_size
          Size of buffers in buffer pool for parameter offloading
          when ``offload_params_device`` is ``nvme``.
  
      max_in_cpu
          Number of parameter elements to maintain in CPU memory when offloading to NVMe is enabled.
  
      nvme_path
          Filesystem path for NVMe device for optimizer/parameter state offloading.
  
      optimizer_buffer_count
          Number of buffers in buffer pool for optimizer state offloading
          when ``offload_optimizer_device`` is set to to ``nvme``.
          This should be at least the number of states maintained per parameter by the optimizer.
          For example, Adam optimizer has 4 states (parameter, gradient, momentum, and variance).
  
      block_size
          When using NVMe Offloading, the I/O block size in bytes.
  
      queue_depth
          When using NVMe Offloading, the I/O queue depth.
  
      single_submit
          When using NVMe Offloading,
          submit requests to storage device as multiple individual requests,
          as opposed to one block of requests.
  
      overlap_events
          When using NVMe Offloading,
          submit requests to storage device in an overlapped fashion
          without waiting for completion of earlier requests.
  
      thread_count
          When using NVMe Offloading,
          Intra-request parallelism for each read/write submitted by a user thread.
  
      pin_memory
          When using ZeRO stage 3, pin optimizer state memory on CPU.
          This could boost throughput at the cost of extra memory overhead.
  
      sub_group_size
          When using ZeRO stage 3, defines the number of parameters
          within a sub group to offload at a time.
          Smaller numbers require more communication, but improve memory efficiency.
  
      contiguous_gradients
          Copies gradients to a continuous buffer as they are produced.
          Avoids memory fragmentation during backwards. Useful when training large models.
  
      overlap_comm
          Overlap the reduction (synchronization) of gradients with the backwards computation.
          This is a speed optimization when training across multiple GPUs/machines.
  
      allgather_partitions: All gather updated parameters at the end of training step,
          instead of using a series of broadcast collectives.
  
      reduce_scatter: Use reduce/scatter instead of allreduce to average gradients.
  
      allgather_bucket_size
          Number of elements to allgather at once.
          Used to limit the memory required for larger model sizes, with a tradeoff with speed.
  
      reduce_bucket_size
          Number of elements to reduce at once.
          Used to limit the memory required for larger model sizes, with a tradeoff with speed.
  
      zero_allow_untested_optimizer
          Allow untested optimizers to be used with ZeRO. Currently only Adam is a
          DeepSpeed supported optimizer when using ZeRO.
  
      logging_batch_size_per_gpu
          Config used in DeepSpeed to calculate verbose timing for logging
          on a per sample per second basis (only displayed if logging=logging.INFO).
          If set to "auto", the plugin tries to infer this from
          the train DataLoader's BatchSampler, else defaults to 1.
          To obtain accurate logs when using datasets that do not support batch samplers,
          set this to the actual per gpu batch size (trainer.batch_size).
  
      config
          Pass in a deepspeed formatted config dict,
          or path to a deepspeed config: https://www.deepspeed.ai/docs/config-json.
          All defaults will be ignored if a config is passed in.
  
      logging_level
          Set logging level for deepspeed.
  
      loss_scale
          Loss scaling value for FP16 training.
          0.0 results in dynamic loss scaling, otherwise static.
  
      initial_scale_power
          Power of the initial dynamic loss scale value. Loss scale is computed
          by ``2^initial_scale_power``.
  
      loss_scale_window
          Window in which to raise/lower the dynamic FP16 loss scaling value.
  
      hysteresis
          FP16 Delay shift in Dynamic Loss scaling.
  
      min_loss_scale
          The minimum FP16 dynamic loss scaling value.
  
      partition_activations
          Enables partition activation when used with ZeRO stage 3 and model parallelism.
          Still requires you to wrap your forward functions in deepspeed.checkpointing.checkpoint.
          See `deepspeed tutorial
          <https://www.deepspeed.ai/tutorials/megatron/#deepspeed-activation-checkpoints-optional>`_.
  
      cpu_checkpointing
          Offloads partitioned activations to CPU if ``partition_activations`` is enabled.
  
      contiguous_memory_optimization
          Copies partitioned activations so that they are contiguous in memory.
          Not supported by all models.
  
      synchronize_checkpoint_boundary
          Insert :func:`torch.cuda.synchronize` at each checkpoint boundary.
  
      load_full_weights
          True when loading a single checkpoint file containing the model state dict
          when using ZeRO Stage 3. This differs from the DeepSpeed checkpoint which contains shards
          per worker.
      """
      super().__init__(
          accelerator=accelerator,
          parallel_devices=parallel_devices,
          cluster_environment=cluster_environment,
          precision_plugin=precision_plugin,
          process_group_backend=process_group_backend,
          zero_optimization=zero_optimization,
          stage=stage,
          remote_device=remote_device,
          offload_optimizer=offload_optimizer,
          offload_parameters=offload_parameters,
          params_buffer_count=params_buffer_count,
          params_buffer_size=params_buffer_size,
          max_in_cpu=max_in_cpu,
          offload_optimizer_device=offload_optimizer_device,
          optimizer_buffer_count=optimizer_buffer_count,
          block_size=block_size,
          queue_depth=queue_depth,
          single_submit=single_submit,
          overlap_events=overlap_events,
          thread_count=thread_count,
          pin_memory=pin_memory,
          sub_group_size=sub_group_size,
          contiguous_gradients=contiguous_gradients,
          overlap_comm=overlap_comm,
          allgather_partitions=allgather_partitions,
          reduce_scatter=reduce_scatter,
          allgather_bucket_size=allgather_bucket_size,
          reduce_bucket_size=reduce_bucket_size,
          zero_allow_untested_optimizer=zero_allow_untested_optimizer,
          logging_batch_size_per_gpu=logging_batch_size_per_gpu,
          config=config,
          logging_level=logging_level,
          loss_scale=loss_scale,
          initial_scale_power=initial_scale_power,
          loss_scale_window=loss_scale_window,
          hysteresis=hysteresis,
          min_loss_scale=min_loss_scale,
          partition_activations=partition_activations,
          cpu_checkpointing=cpu_checkpointing,
          contiguous_memory_optimization=contiguous_memory_optimization,
          synchronize_checkpoint_boundary=synchronize_checkpoint_boundary,
          load_full_weights=load_full_weights,
      )
  
file_path: autogluon/multimodal/src/autogluon/multimodal/optimization/deepspeed.py
imports: import logging
  from typing import Any, Dict, Generator, List, Mapping, Optional, Tuple, Union, cast
  import lightning.pytorch
  import torch
  from lightning.pytorch.accelerators.cuda import CUDAAccelerator
  from lightning.pytorch.overrides.base import _LightningModuleWrapperBase, _LightningPrecisionModuleWrapperBase
  from lightning.pytorch.plugins.environments.cluster_environment import ClusterEnvironment
  from lightning.pytorch.plugins.precision import PrecisionPlugin
  from lightning.pytorch.strategies import DeepSpeedStrategy
  from lightning.pytorch.utilities import GradClipAlgorithmType
  from lightning.pytorch.utilities.exceptions import MisconfigurationException
  from lightning.pytorch.utilities.model_helpers import is_overridden
  from lightning.pytorch.utilities.rank_zero import rank_zero_warn
  from lightning.pytorch.utilities.types import _PATH
label_desc: Use image node_id 1 to create a new CustomDeepSpeedStrategy object from inherited base classes: DeepSpeedStrategy with example: obj = CustomDeepSpeedStrategy(accelerator, zero_optimization, stage, remote_device, offload_optimizer, offload_parameters, offload_params_device, nvme_path, params_buffer_count, params_buffer_size, max_in_cpu, offload_optimizer_device, optimizer_buffer_count, block_size, queue_depth, single_submit, overlap_events, thread_count, pin_memory, sub_group_size, contiguous_gradients, overlap_comm, allgather_partitions, reduce_scatter, allgather_bucket_size, reduce_bucket_size, zero_allow_untested_optimizer, logging_batch_size_per_gpu, config, logging_level, parallel_devices, cluster_environment, loss_scale, initial_scale_power, loss_scale_window, hysteresis, min_loss_scale, partition_activations, cpu_checkpointing, contiguous_memory_optimization, synchronize_checkpoint_boundary, load_full_weights, precision_plugin, process_group_backend)